How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities
Ziwen Xu Zhejiang University, Kewei Xu Zhejiang University, Haoming Xu, Haiwen Hong Alibaba Group, Longtao Huang Alibaba Group, Hui Xue, Ningyu Zhang Zhejiang University, Yongliang Shen Zhejiang University 等 11 位作者
Large Language Models (LLMs) are increasingly deployed in socially sensitive domains, yet their unpredictable behaviors, ranging from misaligned intent to inconsistent personality, pose significant risks. We introduce SteerEval, a hierarchical benchmark for evaluating LLM controllability across three domains: language features, sentiment, and personality. Each domain is structured into three specification levels: L1 (what to express), L2 (how to express), and L3 (how to instantiate), connecting high-level behavioral intent to concrete textual output. Using SteerBench, we systematically evaluate contemporary steering methods, revealing that control often degrades at finer-grained levels. Our benchmark offers a principled and interpretable framework for safe and controllable LLM behavior, serving as a foundation for future research.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表